Sulla base dei lavori svolti dalla Commissione preposta per l’attribuzione del premio per il miglior contributo di “statistica applicata” riservato a giovani statistici – Young Statisticians Award for Statistica Applicata, edizione 2026, e composta dal dott. Yuri Calleo e dai proff. Maurizio Carpita e Francesco Palumbo, è risultata vincitrice e assegnataria del premio di € 1.000 (mille euro) lordi alla candidata Sara Rebottini, per il contributo Understanding healthcare utilization in later life: the role of social and health resources across different European contexts.

Inoltre, la Commissione ha riconosciuto degni di particolare menzione i seguenti candidati ai quali sarà consegnata una targa:

• Luca D’Aniello, per il contributo Educational poverty through the lens of science mapping: a bibliometric analysis of a growing research field
• Sara Selvaggia Guerini, per il contributo A copula-based approach to model traffic flow time series
• Annamaria Porreca, per il contributo Gender-based intertemporal choice profiling: a supervised machine learning framework using augmented functional forests

I vincitori verranno contattati personalmente.

Following the deliberations of the committee responsible for awarding the 2026 Young Statisticians Award for Applied Statistics, composed of Dr. Yuri Calleo and Professors Maurizio Carpita and Francesco Palumbo, the winner of the €1,000 (one thousand euros) gross prize is Sara Rebottini, for her paper “Understanding healthcare utilization in later life: the role of social and health resources across different European contexts.”

In addition, the Committee recognized the following candidates as deserving of special mention. Each will receive a plaque:

• Luca D’Aniello, for the paper “Educational poverty through the lens of science mapping: a bibliometric analysis of a growing research field”
• Sara Selvaggia Guerini, for the paper “A copula-based approach to model traffic flow time series”
• Annamaria Porreca, for the paper “Gender-based intertemporal choice profiling: a supervised machine learning framework using augmented functional forests”

The winners will be contacted personally.